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<div class="titlepage"><div><div><h5 class="title">
<a name="math_toolkit.dist.stat_tut.overview.generic"></a><a class="link" href="generic.html" title="Generic operations common to all distributions are non-member functions"> Generic
operations common to all distributions are non-member functions</a>
</h5></div></div></div>
<p>
Want to calculate the PDF (Probability Density Function) of a distribution?
No problem, just use:
</p>
<pre class="programlisting"><span class="identifier">pdf</span><span class="special">(</span><span class="identifier">my_dist</span><span class="special">,</span> <span class="identifier">x</span><span class="special">);</span> <span class="comment">// Returns PDF (density) at point x of distribution my_dist.
</span></pre>
<p>
Or how about the CDF (Cumulative Distribution Function):
</p>
<pre class="programlisting"><span class="identifier">cdf</span><span class="special">(</span><span class="identifier">my_dist</span><span class="special">,</span> <span class="identifier">x</span><span class="special">);</span> <span class="comment">// Returns CDF (integral from -infinity to point x)
</span> <span class="comment">// of distribution my_dist.
</span></pre>
<p>
And quantiles are just the same:
</p>
<pre class="programlisting"><span class="identifier">quantile</span><span class="special">(</span><span class="identifier">my_dist</span><span class="special">,</span> <span class="identifier">p</span><span class="special">);</span> <span class="comment">// Returns the value of the random variable x
</span> <span class="comment">// such that cdf(my_dist, x) == p.
</span></pre>
<p>
If you're wondering why these aren't member functions, it's to make the
library more easily extensible: if you want to add additional generic
operations - let's say the <span class="emphasis"><em>n'th moment</em></span> - then all
you have to do is add the appropriate non-member functions, overloaded
for each implemented distribution type.
</p>
<div class="tip"><table border="0" summary="Tip">
<tr>
<td rowspan="2" align="center" valign="top" width="25"><img alt="[Tip]" src="../../../../../../../../../doc/src/images/tip.png"></td>
<th align="left">Tip</th>
</tr>
<tr><td align="left" valign="top">
<p>
</p>
<p>
<span class="bold"><strong>Random numbers that approximate Quantiles of
Distributions</strong></span>
</p>
<p>
If you want random numbers that are distributed in a specific way,
for example in a uniform, normal or triangular, see <a href="http://www.boost.org/libs/random/" target="_top">Boost.Random</a>.
</p>
<p>
Whilst in principal there's nothing to prevent you from using the quantile
function to convert a uniformly distributed random number to another
distribution, in practice there are much more efficient algorithms
available that are specific to random number generation.
</p>
</td></tr>
</table></div>
<p>
For example, the binomial distribution has two parameters: n (the number
of trials) and p (the probability of success on one trial).
</p>
<p>
The <code class="computeroutput"><span class="identifier">binomial_distribution</span></code>
constructor therefore has two parameters:
</p>
<p>
<code class="computeroutput"><span class="identifier">binomial_distribution</span><span class="special">(</span><span class="identifier">RealType</span>
<span class="identifier">n</span><span class="special">,</span>
<span class="identifier">RealType</span> <span class="identifier">p</span><span class="special">);</span></code>
</p>
<p>
For this distribution the random variate is k: the number of successes
observed. The probability density/mass function (pdf) is therefore written
as <span class="emphasis"><em>f(k; n, p)</em></span>.
</p>
<div class="note"><table border="0" summary="Note">
<tr>
<td rowspan="2" align="center" valign="top" width="25"><img alt="[Note]" src="../../../../../../../../../doc/src/images/note.png"></td>
<th align="left">Note</th>
</tr>
<tr><td align="left" valign="top">
<p>
</p>
<p>
<span class="bold"><strong>Random Variates and Distribution Parameters</strong></span>
</p>
<p>
<a href="http://en.wikipedia.org/wiki/Random_variate" target="_top">Random variates</a>
and <a href="http://en.wikipedia.org/wiki/Parameter" target="_top">distribution
parameters</a> are conventionally distinguished (for example in
Wikipedia and Wolfram MathWorld by placing a semi-colon (or sometimes
vertical bar) after the random variate (whose value you 'choose'),
to separate the variate from the parameter(s) that defines the shape
of the distribution.
</p>
</td></tr>
</table></div>
<p>
As noted above the non-member function <code class="computeroutput"><span class="identifier">pdf</span></code>
has one parameter for the distribution object, and a second for the random
variate. So taking our binomial distribution example, we would write:
</p>
<p>
<code class="computeroutput"><span class="identifier">pdf</span><span class="special">(</span><span class="identifier">binomial_distribution</span><span class="special"><</span><span class="identifier">RealType</span><span class="special">>(</span><span class="identifier">n</span><span class="special">,</span> <span class="identifier">p</span><span class="special">),</span> <span class="identifier">k</span><span class="special">);</span></code>
</p>
<p>
The ranges of random variate values that are permitted and are supported
can be tested by using two functions <code class="computeroutput"><span class="identifier">range</span></code>
and <code class="computeroutput"><span class="identifier">support</span></code>.
</p>
<p>
The distribution (effectively the random variate) is said to be 'supported'
over a range that is <a href="http://en.wikipedia.org/wiki/Probability_distribution" target="_top">"the
smallest closed set whose complement has probability zero"</a>.
MathWorld uses the word 'defined' for this range. Non-mathematicians
might say it means the 'interesting' smallest range of random variate
x that has the cdf going from zero to unity. Outside are uninteresting
zones where the pdf is zero, and the cdf zero or unity.
</p>
<p>
For most distributions, with probability distribution functions one might
describe as 'well-behaved', we have decided that it is most useful for
the supported range to exclude random variate values like exact zero
<span class="bold"><strong>if the end point is discontinuous</strong></span>. For
example, the Weibull (scale 1, shape 1) distribution smoothly heads for
unity as the random variate x declines towards zero. But at x = zero,
the value of the pdf is suddenly exactly zero, by definition. If you
are plotting the PDF, or otherwise calculating, zero is not the most
useful value for the lower limit of supported, as we discovered. So for
this, and similar distributions, we have decided it is most numerically
useful to use the closest value to zero, min_value, for the limit of
the supported range. (The <code class="computeroutput"><span class="identifier">range</span></code>
remains from zero, so you will still get <code class="computeroutput"><span class="identifier">pdf</span><span class="special">(</span><span class="identifier">weibull</span><span class="special">,</span> <span class="number">0</span><span class="special">)</span>
<span class="special">==</span> <span class="number">0</span></code>).
(Exponential and gamma distributions have similarly discontinuous functions).
</p>
<p>
Mathematically, the functions may make sense with an (+ or -) infinite
value, but except for a few special cases (in the Normal and Cauchy distributions)
this implementation limits random variates to finite values from the
<code class="computeroutput"><span class="identifier">max</span></code> to <code class="computeroutput"><span class="identifier">min</span></code> for the <code class="computeroutput"><span class="identifier">RealType</span></code>.
(See <a class="link" href="../../../backgrounders/implementation.html#math_toolkit.backgrounders.implementation.handling_of_floating_point_infinity">Handling
of Floating-Point Infinity</a> for rationale).
</p>
<div class="note"><table border="0" summary="Note">
<tr>
<td rowspan="2" align="center" valign="top" width="25"><img alt="[Note]" src="../../../../../../../../../doc/src/images/note.png"></td>
<th align="left">Note</th>
</tr>
<tr><td align="left" valign="top">
<p>
</p>
<p>
<span class="bold"><strong>Discrete Probability Distributions</strong></span>
</p>
<p>
Note that the <a href="http://en.wikipedia.org/wiki/Discrete_probability_distribution" target="_top">discrete
distributions</a>, including the binomial, negative binomial, Poisson
& Bernoulli, are all mathematically defined as discrete functions:
that is to say the functions <code class="computeroutput"><span class="identifier">cdf</span></code>
and <code class="computeroutput"><span class="identifier">pdf</span></code> are only defined
for integral values of the random variate.
</p>
<p>
However, because the method of calculation often uses continuous functions
it is convenient to treat them as if they were continuous functions,
and permit non-integral values of their parameters.
</p>
<p>
Users wanting to enforce a strict mathematical model may use <code class="computeroutput"><span class="identifier">floor</span></code> or <code class="computeroutput"><span class="identifier">ceil</span></code>
functions on the random variate prior to calling the distribution function.
</p>
<p>
The quantile functions for these distributions are hard to specify
in a manner that will satisfy everyone all of the time. The default
behaviour is to return an integer result, that has been rounded <span class="emphasis"><em>outwards</em></span>:
that is to say, lower quantiles - where the probablity is less than
0.5 are rounded down, while upper quantiles - where the probability
is greater than 0.5 - are rounded up. This behaviour ensures that if
an X% quantile is requested, then <span class="emphasis"><em>at least</em></span> the
requested coverage will be present in the central region, and <span class="emphasis"><em>no
more than</em></span> the requested coverage will be present in the
tails.
</p>
<p>
This behaviour can be changed so that the quantile functions are rounded
differently, or return a real-valued result using <a class="link" href="../../../policy/pol_overview.html" title="Policy Overview">Policies</a>.
It is strongly recommended that you read the tutorial <a class="link" href="../../../policy/pol_tutorial/understand_dis_quant.html" title="Understanding Quantiles of Discrete Distributions">Understanding
Quantiles of Discrete Distributions</a> before using the quantile
function on a discrete distribtion. The <a class="link" href="../../../policy/pol_ref/discrete_quant_ref.html" title="Discrete Quantile Policies">reference
docs</a> describe how to change the rounding policy for these distributions.
</p>
<p>
For similar reasons continuous distributions with parameters like "degrees
of freedom" that might appear to be integral, are treated as real
values (and are promoted from integer to floating-point if necessary).
In this case however, there are a small number of situations where
non-integral degrees of freedom do have a genuine meaning.
</p>
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<td align="right"><div class="copyright-footer">Copyright © 2006 , 2007, 2008, 2009 John Maddock, Paul A. Bristow,
Hubert Holin, Xiaogang Zhang, Bruno Lalande, Johan Råde, Gautam Sewani
and Thijs van den Berg<p>
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at <a href="http://www.boost.org/LICENSE_1_0.txt" target="_top">http://www.boost.org/LICENSE_1_0.txt</a>)
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